Multi-objective Robust Optimization for Integrated Energy Systems Including Economics and Carbon Emissions

Under the influence of dual carbon, the traditional single-objective economic optimization dispatch method has been difficult to meet the requirements of optimal system operation, and carbon emission has become an integral component that cannot be neglected. This paper explores cooling, heat and electricity including renewables in integrated energy systems. The paper considers two indicators, operating cost and carbon emissions, as the objective function of the multi-objective optimization problem, a multi-objective robust optimal scheduling model is developed for integrated energy systems that takes into account the uncertainty of renewable energy supply and load. By applying the dual theory, the multi-objective robust optimization model is changed into a multi-objective deterministic model, and the Pareto problem is handled by employing compromise programming and max-min fuzzy technique to determine the point of equilibrium between economic and environmental advantages. The numerical experiment illustrate the accuracy of the model and the efficacy of the strategy for addressing multi-objective robust optimization.